weather-sculpt

Model · Other

JLD

Shreshth

Official fitted Jacobian lens for JLD and JLD-fast, by Shreshth Saini, Balu Adsumilli, and Alan C. Bovik. JLD measures the perceptual distance between an aligned reference image and a distorted image. Lower scores mean more similar images. It projects early DINOv2 patch features onto directions selected by the encoder's output sensitivity. The lens is fitted from unlabeled images without human quality ratings. This installs the versioned release wheel and records it in uv.lock. The distribution is named jacobian-lens-distance; the Python import remains jld. The package already includes this lens. The encoder weights are downloaded separately through timm on first use. Python 3.10 or newer…

Model · Other

cua-s1-forms-onnx

Mohamed yasser

ONNX conversion of cua-ai/cua-s1-forms, prepared for lightweight local inference experiments with ONNX Runtime, ONNX Runtime Web, WebAssembly (WASM), and WebGPU. Please read the original model card before using this conversion. It contains the authoritative description of the model's architecture, training setup, evaluation, limitations, and intended scope. CUA-S1 Forms is a small specialist "System One" model for form-oriented computer-use tasks. Unlike an autoregressive LLM, it does not generate text token-by-token. For each UI element, it receives: 1. a short structured context describing the task, form, and element, and 2. a set of candidate options such as extracted document values…

A frozen, non-learning recursive network built on the real fruit-fly MaleCNS connectome (Janelia 2024). Feed any stimulus, get an organic, structured "instinctive" signal that is more alive than noise, less predictable than a script. Not AI. Not a brain. Not learning. Just real neural wiring, frozen. - Real MaleCNS connectome subset (1,587,930 edges, ~25 MB) - 4 tuned presets: escape / sugar / startle / explore - Zero training. Weight-frozen. Deterministic per seed. https://roblitz-spec.github.io/fly-instinct/ - Full 1.05GB connectome + commercial license: https://robloxer31.gumroad.com/l/Fly-Instinct

Model · Other

TTVidT-decoders

Shih-Ying Yeh

Pretrained DiT decoders for TT-VidT (Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining, NeurIPS 2026). Paper · Each TT-VidT encoder pretraining run initialises its decoder from one of these; the training code downloads them automatically through DECODERPRETRAINED, e.g. "KBlueLeaf/TTVidT-decoders/pretrainvideoSqknorm". The decoders work in the 32x32x4 latent space of the frozen f8 frame VAE KBlueLeaf/latentmaid-vae and are conditioned on DINOv3 ViT-B/16 features (768-d). - ImageNet decoders reconstruct an ImageNet image from its DINOv3 class token. - Video decoders reconstruct a later video frame from its class token plus, through cross-attention, the spatial…

Model · Other

nys-sft-public

Dakuwon Moody

NYS is not a transformer. It is a coupled-oscillator organism: phases \(\thetai\), amplitudes, natural frequencies \(\omegai\), and a sparse coupling graph \(K\). Language, compiler bits, and speech-frame IDs live on one graph (\(N = 5{,}000{,}000\) nodes, \(k = 512\) edges per node). The native graph trains by Equilibrium Propagation (free RK4, then same-moment free/nudged branches, then a contrastive \(K\) update), without backprop or a cross-entropy objective. A separate sparse context head now provides retrieved language inputs. The GPU Qwen teacher uses PyTorch/Transformers; that does not make the native graph a transformer. Dakuwon Moody (YNSScarSaiyan) · Saiyan Corp The…

Model · Other

coreml-physicsnemo

Alex Pospekhov

Reference Machine: Apple M3 Max (16 CPU cores, 40 Metal GPU cores, 16 Apple Neural Engine cores, 48 GB Unified Memory) - Unlocking the Apple Neural Engine for Physical AI: While Apple's 16-Core NPU (38 TOPS) was designed primarily for vision and speech, physical PDE operators historically failed to run on it due to unsupported tensor primitives. By algebraically reformulating complex Fourier spectral convolutions and dynamic graph scatter into native Apple Machine Intermediate Language (MIL) representations, all 8 foundation networks achieve up to 98.4% ANE execution with 0 CPU fallbacks. - Extreme Energy Efficiency (0.071 J / step for ANE aerodynamic solver to 10.8 J for global weather at…